code: def forward(self, x):
      x = F.relu(self.conv1(x))
      x = F.max_pool2d(self.conv2(x), 2)
      x = torch.flatten(self.dropout1(x), 1)
      x = self.fc2(self.dropout2(F.relu(self.fc1(x))))
      output = F.log_softmax(x, dim=1)
      return output
  
imports: import torch
  import torch.nn
  import torch.nn.functional
  import nni
  from nni.nas.nn.pytorch import LayerChoice, ModelSpace, MutableDropout, MutableLinear
  import nni.nas.strategy
  import nni
  from torchvision import transforms
  from torchvision.datasets import MNIST
  from torch.utils.data import DataLoader
  from nni.nas.evaluator import FunctionalEvaluator
  from nni.nas.experiment import NasExperiment
  import os
  from pathlib import Path
label_desc: Use image node_id 2 for calling the MyModelSpace obj's underlying member method code with example usage: obj.forward(x) and returns: output